Acrobatic geckos can sprint at high speeds over challenging terrain {$[$}1{$]$}, scamper up the smoothest surfaces {$[$}2{$]$}, rapidly swing underneath leaves {$[$}3{$]$}, and right themselves in midair by swinging only their tails {$[$}4, 5{$]$}. From our field observations, we can add racing on the water?s surface to the gecko?s list of agile feats. Locomotion at the air-water interface evolved in over a thousand species, including insects, fish, reptiles, and mammals {$[$}6{$]$}. To support their weight, some larger-legged vertebrates use forces generated by vigorous slapping of the fluid?s surface followed by a stroke of their appendage {$[$}7?12{$]$}, whereas smaller animals, like arthropods, rely on surface tension to walk on water {$[$}6, 13{$]$}. Intermediate-sized geckos (Hemidactylus platyurus) fall squarely between these two regimes. Here, we report the unique ability of geckos to exceed the speed limits of conventional surface swimming. Several mechanisms likely contribute in this intermediate regime. In contrast to bipedal basilisk lizards {$[$}7?10{$]$}, geckos used a stereotypic trotting gait with all four limbs, creating air cavities during slapping to raise their head and anterior trunk above water. Adding surfactant to the water decreased velocity by half, confirming surface tension?s role. The superhydrophobic skin could reduce drag during semi-planing. Geckos laterally undulated their bodies, including their submerged posterior trunk and tail, generating thrust for forward propulsion, much like water dragons {$[$}14{$]$} and alligators {$[$}15{$]$}. Geckos again remind us of the advantages of multi-functional morphologies providing the opportunity for multiple mechanisms for motion.

We present an effective dynamic clustering algorithm for the task of temporal human action segmentation, which has comprehensive applications such as robotics, motion analysis, and patient monitoring. Our proposed algorithm is unsupervised, fast, generic to process various types of features, and applica- ble in both the online and offline settings. We perform extensive experiments of processing data streams, and show that our algorithm achieves the state-of- the-art results for both online and offline settings.

Proceedings of the National Academy of Sciences, 115, pages: E8358-E8367, 2018 (article)

Abstract

During dynamic terrestrial locomotion, animals use complex multifunctional feet to extract friction from the environment. However, whether roboticists assume sufficient surface friction for locomotion or actively compensate for slipping, they use relatively simple point-contact feet. We seek to understand and extract the morphological adaptations of animal feet that contribute to enhancing friction on diverse surfaces, such as the desert locust (Schistocerca gregaria) [Bennet-Clark HC (1975) J Exp Biol 63:53–83], which has both wet adhesive pads and spines. A buckling region in their knee to accommodate slipping [Bayley TG, Sutton GP, Burrows M (2012) J Exp Biol 215:1151–1161], slow nerve conduction velocity (0.5–3 m/s) [Pearson KG, Stein RB, Malhotra SK (1970) J Exp Biol 53:299–316], and an ecological pressure to enhance jumping performance for survival [Hawlena D, Kress H, Dufresne ER, Schmitz OJ (2011) Funct Ecol 25:279–288] further suggest that the locust operates near the limits of its surface friction, but without sufficient time to actively control its feet. Therefore, all surface adaptation must be through passive mechanics (morphological intelligence), which are unknown. Here, we report the slipping behavior, dynamic attachment, passive mechanics, and interplay between the spines and adhesive pads, studied through both biological and robotic experiments, which contribute to the locust’s ability to jump robustly from diverse surfaces. We found slipping to be surface-dependent and common (e.g., wood 1.32 ± 1.19 slips per jump), yet the morphological intelligence of the feet produces a significant chance to reengage the surface (e.g., wood 1.10 ± 1.13 reengagements per jump). Additionally, a discovered noncontact-type jump, further studied robotically, broadens the applicability of the morphological adaptations to both static and dynamic attachment.

We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics. Recent approaches are either data-driven or learning-based: Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations; Learning-based approaches, in contrast, avoid the expensive optimization step by learning to directly predict complete shapes from incomplete observations in a fully-supervised setting. However, full supervision is often not available in practice. In this work, we propose a weakly-supervised learning-based approach to 3D shape completion which neither requires slow optimization nor direct supervision. While we also learn a shape prior on synthetic data, we amortize, i.e., learn, maximum likelihood fitting using deep neural networks resulting in efficient shape completion without sacrificing accuracy. On synthetic benchmarks based on ShapeNet and ModelNet as well as on real robotics data from KITTI and Kinect, we demonstrate that the proposed amortized maximum likelihood approach is able to compete with a fully supervised baseline and outperforms the data-driven approach of Engelmann et al., while requiring less supervision and being significantly faster.

This work investigates the estimation of dense three-dimensional motion fields, commonly
referred to as scene flow. While great progress has been made in recent years,
large displacements and adverse imaging conditions as observed in natural outdoor
environments are still very challenging for current approaches to reconstruction and
motion estimation. In this paper, we propose a unified random field model which reasons
jointly about 3D scene flow as well as the location, shape and motion of vehicles
in the observed scene. We formulate the problem as the task of decomposing the scene
into a small number of rigidly moving objects sharing the same motion parameters.
Thus, our formulation effectively introduces long-range spatial dependencies which
commonly employed local rigidity priors are lacking. Our inference algorithm then
estimates the association of image segments and object hypotheses together with their
three-dimensional shape and motion. We demonstrate the potential of the proposed
approach by introducing a novel challenging scene flow benchmark which allows for a
thorough comparison of the proposed scene flow approach with respect to various baseline
models. In contrast to previous benchmarks, our evaluation is the first to provide
stereo and optical flow ground truth for dynamic real-world urban scenes at large scale.
Our experiments reveal that rigid motion segmentation can be utilized as an effective
regularizer for the scene flow problem, improving upon existing two-frame scene flow
methods. At the same time, our method yields plausible object segmentations without requiring an explicitly trained recognition model for a specific object class.

Living organisms from single cells to humans need to adapt continuously to respond to changes in their environment. The process of behavioural adaptation can be thought of as improving decision-making performance according to some utility function. Here, we consider an abstract model of organisms as decision-makers with limited information-processing resources that trade off between maximization of utility and computational costs measured by a relative entropy, in a similar fashion to thermodynamic systems undergoing isothermal transformations. Such systems minimize the free energy to reach equilibrium states that balance internal energy and entropic cost. When there is a fast change in the environment, these systems evolve in a non-equilibrium fashion because they are unable to follow the path of equilibrium distributions. Here, we apply concepts from non-equilibrium thermodynamics to characterize decision-makers that adapt to changing environments under the assumption that the temporal evolution of the utility function is externally driven and does not depend on the decision-maker’s action. This allows one to quantify performance loss due to imperfect adaptation in a general manner and, additionally, to find relations for decision-making similar to Crooks’ fluctuation theorem and Jarzynski’s equality. We provide simulations of several exemplary decision and inference problems in the discrete and continuous domains to illustrate the new relations.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems